TURIN: A coding system for Trust in hUman Robot INteraction

Abstract

A natural human-robot interaction (HRI) relies on the robot’s capacity to understand the users’ behaviors through psychological and sociological concepts. Users expect the robot to act in a realistic manner to create a more human-like relationship. In this context, trust is an essential concept as it determines the effectiveness of the system and its acceptance by users. The understanding of trust dynamics in HRI is still low and systematic studies of multimodal trust-related behaviors in HRI are relatively rare given the rising popularity of the topic. To bridge this gap, in this paper we present a novel coding system TURIN (Trust in hUman Robot INteraction) to study trust in HRI. A preliminary assessment of the coding system was carried out on the Vernissage dataset. Results show a significant agreement between expert annotators.